{"id":"W4223558080","doi":"10.1093/humrep/deac072","title":"#ESHREjc report: seeing is believing! How time lapse imaging can improve IVF practice and take it to the future clinic","year":2022,"lang":"en","type":"article","venue":"Human Reproduction","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial insemination; Assisted reproductive technology; Embryo transfer; Human fertilization; Gynecology; Embryo; Artificial intelligence; Biology; Medicine; Computer science; Infertility; Pregnancy; Anatomy; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00541793,0.0005666356,0.0005167575,0.001446805,0.001900333,0.003811255,0.001049716,0.003829126,0.1233139],"category_scores_gemma":[0.02271599,0.0002913451,0.0005673054,0.0008055657,0.00100889,0.002052858,0.003429941,0.004035444,0.06066291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001748082,"about_ca_system_score_gemma":0.003710877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004963872,"about_ca_topic_score_gemma":0.00754342,"domain_scores_codex":[0.9964427,0.0007990336,0.0002857025,0.0002768828,0.001779714,0.0004159608],"domain_scores_gemma":[0.9823009,0.004272639,0.0006551691,0.0007130837,0.008212009,0.003846128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001804429,0.00001250014,0.0004403522,0.00005612688,0.000002466744,0.0001580096,0.00009788264,0.000009003617,0.00006882715,0.0003007861,0.983235,0.01560106],"study_design_scores_gemma":[0.000005360767,0.00002318911,0.001531507,0.0001713594,0.000003369782,0.0003747782,0.0003844835,0.00002536834,0.000119754,0.0002544844,0.9970934,0.00001283229],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.00712387,0.01379719,0.00270904,0.6267417,0.1889792,0.000437451,0.009015255,0.001434818,0.1497615],"genre_scores_gemma":[0.04175162,0.02103667,0.004016846,0.2440004,0.07582602,0.000627641,0.01032663,0.002149128,0.600265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1233139,"threshold_uncertainty_score":0.4125261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974541786061206,"score_gpt":0.3091059387603278,"score_spread":0.2893605208997158,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}